GraphNews

Graphs Neural Networks (GNNs) and LLMs are colliding in exciting ways. 💥 This survey introduces a novel taxonomy for categorizing existing methods that… | 19 comments on LinkedIn

“Ever wondered how to find or build Knowledge Graphs that power everything from recommendation engines to drug discovery?”

The 3 layers of Agentic Graph RAG 💬 The 3 layers of agentic graph RAG represent a significant advancement in AI-driven knowledge systems. These layers… | 17 comments on LinkedIn

Researchers created a knowledge graph based on 58 million journal papers to fuel personalized research ideas for scientists. Over 4400 ideas generated by the… | 29 comments on LinkedIn

Hello wonderful people, I’ve noticed some common misconceptions about graphs and AI circulating on LinkedIn, and I thought it might be helpful to share some insights to clarify these topics. I hope…

Glean | Glean’s knowledge graph and signals enables it to provide state-of-the-art hybrid search and generative AI that only gets better over time.

In this series of posts I make an appeal to organize global financial institutions and RiskTech vendors to collaborate on an open-source repository of financial risk patterns called network motifs to…

🧠 Knowledge-driven data access means accessing data through an ontology used to represent its meaning at a conceptual level. 🔎 Connecting an enterprise…

I work with graphs every day. This simple question left me speechless. During a meeting with a customer I started a sentence saying "In graph theory this… | 28 comments on LinkedIn

If you care about sustainability, I have a data challenge to pull your hair out. What is the path in this supply chain that leads to the least CO2 emissions?…

A curated list of resources for graph-related topics, including graph databases, analytics and science - graphgeeks-lab/awesome-graph-universe

Simplifying Complex Graphs with Nested Nodes and Edges: A Challenge Navigating massive graphs with nested nodes, including groups within groups, can feel like… | 34 comments on LinkedIn

A recent post I wrote on LinkedIn hit a nerve. Talking about information/semantic rich projects failing due to the lack of organizational commitment is not new, for me or others in the field.

📣 Foundation models for graph reasoning become even stronger - in our new NeurIPS 2024 work we introduce UltraQuery: going beyond simple one-hop link…

As a technology SME in the KM space, I am excited about the changes happening in the semantic software industry. Just two years ago, in my book, I provided a complete analysis of the leading providers of taxonomy and ontology management systems, as well as graph providers, auto-tagging systems, and more. While the software products I evaluated are still around, most of them have new owners.

RDF vs LPG: Friends or Foes? For over a decade, ever since #KnowledgeGraphs (KGs) gained prominence, there has been intense competition between #RDF (also…

After a period of more than a year (can't believe time flew by so quick!), I had the pleasure of going back for a second time on the Practical AI Podcast with…

We have some pretty exciting news! We have filed an international patent on our new graph database query algorithm. We have also done some further benchmarking tests, and the results are pretty astounding.

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Cosdata: A cutting-edge AI data platform for next-gen search pipelines. Features semantic search, knowledge graphs, hybrid capabilities, real-time scalability, and ML integration. Designed for immu...

Recently, knowledge-graph-enhanced recommendation systems have attracted much attention, since knowledge graph (KG) can help improving the dataset quality and offering rich semantics for explainable recommendation. However, current KG-enhanced solutions focus on analyzing user behaviors on the product level and lack effective approaches to extract user preference towards product category, which is essential for better recommendation because users shopping online normally have strong preference towards distinctive product categories, not merely on products, according to various user studies. Moreover, the existing pure embedding-based recommendation methods can only utilize KGs with a limited size, which is not adaptable to many real-world applications. In this paper, we generalize the recommendation problem with preference mining as a compound knowledge reasoning task and propose a novel multi-agent system, called Mcore, which can promote model performance by mining users’ high-level interests and is adaptable to large KGs. Specifically, we split the overall problem and allocate sub-task to each agent: Coordinate Agent takes charge of recognizing the product-category preference of current user, while Relation Agent and Entity Agent perform KG reasoning cooperatively from a user node towards the preferred categories and terminate at a product node as recommendation. To train this heterogeneous multi-agent system, where agents own various functionalities, we propose an asynchronous reinforcement training pipeline, called Multi-agent Collaborative Learning. The extensive experiments on real datasets demonstrate the effectiveness and adaptability of Mcore on recommendation tasks.

In this work, we achieve perfect neural execution of several algorithms by forcing the node and edge representations to be from a fixed finite set. Also, the proposed architectural choice allows us to prove the correctness of the learned algorithms for any test data.

Author Dan Selman shows how easy it is to implement convert natural language text to nodes and edges in a knowledge graph using a new class and method in his demonstration project.

Implementing Semantic Data Products: A Comprehensive Blueprint for Success As we conclude our series on semantic data products, let's put all the pieces… | 15 comments on LinkedIn

Knowledge Graph / Concept Model: Same or Different? In my understanding when people say 'knowledge graph' they are usually talking about something OWL/RDF-ish,… | 42 comments on LinkedIn

💡 The relevance, trustworthiness and quality of AI and #GenAI applications is increasingly dependent on the quality of enterprise private data and documents…

1. Where does a knowledge graph fit in an enterprise data architecture? A knowledge graph typically sits above data entry/storage systems and below data consumption/visualization systems.